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arXiv · 2208.04076

Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing

Heng Cong·Rongyu Zhang·Jiarong He·Jin Gao
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Abstract

Face anti-spoofing researches are widely used in face recognition and has received more attention from industry and academics. In this paper, we propose the EulerNet, a new temporal feature fusion network in which the differential filter and residual pyramid are used to extract and amplify abnormal clues from continuous frames, respectively. A lightweight sample labeling method based on face landmarks is designed to label large-scale samples at a lower cost and has better results than other methods such as 3D camera. Finally, we collect 30,000 live and spoofing samples using various mobile ends to create a dataset that replicates various forms of attacks in a real-world setting. Extensive experiments on public OULU-NPU show that our algorithm is superior to the state of art and our solution has already been deployed in real-world systems servicing millions of users.

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Heng Cong, Rongyu Zhang, Jiarong He, Jin Gao. 2022-08-08. Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing. https://doi.org/10.18293/seke2022-076

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